Introduction

• Missing data in real-world datasets is a key challenge.
• Causes: Processing errors, measurement errors, survey non-responses, invalid calculations, participant dropout.
• Impact: Reduces usable observations, introduces bias, interferes with analysis tools.
• Importance: Accurate handling is critical for reliable data analysis.

Identifying Missing Data

• Representation: Standard (NA, NaN, Null, None), strings (blanks, spaces, empty strings), numerical placeholders (extreme values like 999999).
• Useful R Functions: is.na() counts standard missing values, unique() identifies unexpected strings.
• Visual Tools: Histograms for numerical placeholders.
• Example: Survey dataset with missing age values represented as blanks.

Missing Data Mechanisms

• MCAR (Missing Completely at Random): Independent of observed and unobserved data. Example: Random network connectivity loss. Impact: Deletion reduces data size, no bias.
• MAR (Missing at Random): Depends on observed data. Example: Age omission in surveys by specific demographics. Impact: Potential bias if not handled.
• MNAR (Missing Not at Random): Related to unobserved data. Example: Health survey non-responses due to severity of illness. Impact: Complex analysis, prone to bias.

Techniques for Handling Missing Data

Deletion Methods:

• Listwise Deletion: Removes rows with any missing values. Simple but reduces dataset size.
• Feature Selection: Removes columns with high proportions of missing data. Maintains observation count but reduces feature count.
• Example: Removing rows with missing income data in a financial dataset.

Single Imputation:

• Mean/Median Imputation: Replaces missing values with the mean/median of the column.
• Hot Deck Imputation: Uses values from similar records to fill missing data.
• Regression-based Imputation: Predicts missing values using regression models based on other variables.
• Example: Replacing missing temperature readings with the average temperature of the day.

Multiple Imputation:

• MICE (Multiple Imputation by Chained Equations): Iteratively imputes missing data by predicting and incorporating uncertainty.
• Process: Generates multiple datasets, imputes missing values, combines results for final analysis.
• Example: Using MICE to impute missing survey responses across several related questions.

Theories for Missing Data

Rubin’s Missing Data Theory:

• Categories: MCAR, MAR, MNAR.
• Framework for understanding mechanisms behind missing data.
• Guides selection of imputation strategies based on these mechanisms.

Statistical Theory of Imputation:

• Uses statistical models to estimate missing values.
• Multiple imputation generates multiple plausible values for each missing data point.
• Example: Using multiple imputation to handle missing financial data in economic research.

Machine Learning Theories:

• Employ techniques like k-nearest neighbors (KNN) and neural networks.
• Predict missing values based on patterns in observed data.
• Example: Using KNN to fill in missing entries in a customer database.

Research Methods for Missing Data

Descriptive Studies:

• Analyze patterns of missing data.
• Example: Jerez et al. analyzed missing data in medical records.

Comparative Studies:

• Evaluate the performance of different imputation methods.
• Example: Ibrahim and Zheng compared methods in clinical trial data.

Simulation Studies:

• Test imputation methods on controlled datasets.
• Allow systematic observation of the impact of different techniques.
• Example: Using simulated datasets to evaluate the accuracy of imputation methods.

Application-Based Studies:

• Real-world implementation examples.
• Example: Saqib Ejaz Awan demonstrated imputation methods in large-scale survey data.

Classification of Data

Imputation Methods

Imputation Methods

Model-Fitting: Decision Tree

Model-Fitting - Random Forest

Model-Fitting - KNN

Introduction to the Dataset

• “Ozone: Los Angeles Ozone Pollution Data, 1976” from mlbench package in R (“Ozone”)
• It contains observations related to pollution levels in the Los Angeles area during 1976.
• It contains a total of 13 variables, 366 observations (one for each day for one year)
• We chose this dataset due to the high volume of already-missing data
• Demonstration of a real-life scenario, where missing values are truly unknown and effectiveness of imputation methods cannot directly be measured.
• It is up to the investigator to choose a methodology for handling the missing data and appropriate metrics for evaluating effectiveness of missing data methods

Variables in the Ozone Dataset

• V1: Month, 1-12, where 1 is January and 12 is December
• V2: Day of month
• V3: Day of week, 1-7, where 1 is Monday and 7 is Sunday
• V4: Daily maximum one-hour-average ozone reading
• V5: 500 millibar pressure height (m) measured at Vandenberg AFB
• V6: Wind speed (mph) at Los Angeles International Airport (LAX)
• V7: Humidity (%) at LAX
• V8: Temperature (degrees F) measured at Sandburg, CA
• V9: Temperature (degrees F) measured at El Monte, CA
• V10: Inversion base height (feet) at LAX
• V11: Pressure gradient (mm Hg) from LAX to Daggett, CA
• V12: Inversion base temperature (degrees F) at LAX
• V13: Visibility (miles) measured at LAX

Exploring Collinearity in the Dataset

• Correlation plot shows collinearity between several of the features in then dataset
• There are a number of predictors that show |r| > 0.5
• Temperature measurements and pressure notably have high collinearity

Exploration of the Missing Data

• The total number of missing data points for each column is shown below as a count and as a percentage.
• Most of the columns contain <5% missing values. Temp_EM contains 139 missing values, which is 38.0% of the observations.
• The total number of missing values in the dataset is 203 out of 4,768 (13 columns times 366 observations). This represents 4.3% of the entire dataset.
• There are a few instances where more than one column has missing data, but the majority of rows with missing values are only missing Temp_EM

Statistical Testing for MCAR (Missing Completely at Random)

• The MissMech package was used to test for MCAR
• First, check for normality of dependent variable (ozone levels)
• If normally distributed, use Hawkin’s test
• If not normally distributed, use non-parametric test
• Shapiro-Wilk and Anderson Darling tests indicate non-normality (p < 0.05)
• Q-Q plot and histogram visibly show non-normal distribution
• non-parametric test p-value: 0.27, fail to reject null hypothesis that data are MCAR

Further Visualization of the Missingess Pattern

Missing Data Deletion or Imputation

• Listwise deletion (delete rows with missing data)
• Feature selection (delete columns with >20% missing data) -> Temp_EM column
• Mean, median, or mode imputation
• MICE
• Random Forest
• A total of 7 datasets were created using various methods for handling missing data

Model-Fitting of Imputed Datasets

• Seven missing data methods -> Seven datasets
• Each dataset was split into train/test datasets using a split ratio of 0.75
• Models were fit for Ozone levels using random forest, decision tree, or KNN algorithms
• Resulting RMSE for test data was used as a metric for best missing data methodology

Model Performance for Each Missing Data Methodology

• The RMSE for each model and imputation method combination was summarized into a data frame
• The best model fit (lowest RMSE) was obtained when using feature selection (column deletion) for missing data and the random forest algorithm for model training
• Simple methods likely outperformed more advanced methods in this case since statistical testing supports that data are MCAR
• There is no single best method for handling missing data, it will always depend on the context
• Testing of multiple methods is the typical approach

          Model  ImpMethod     RMSE
1  RandomForest    DropCol 3.659657
3  DecisionTree       Mean 4.266184
19 RandomForest       Mode 4.367134
16 RandomForest     DropNA 4.493497
4  RandomForest     Median 4.591508
7  RandomForest       MICE 4.901004
10 RandomForest       Mean 5.073677
21 DecisionTree       MICE 5.077593
9  DecisionTree     DropNA 5.213247
12 DecisionTree       Mode 5.249752
13 RandomForest missForest 5.398429
18 DecisionTree     Median 5.537124
2           KNN     DropNA 5.548309
6  DecisionTree missForest 5.799608
8           KNN    DropCol 6.117349
17          KNN       Mean 6.118703
20          KNN missForest 6.142781
5           KNN       Mode 6.161134
11          KNN     Median 6.298989
14          KNN       MICE 6.371706
15 DecisionTree    DropCol 6.603588

Model Performance for Each Missing Data Methodology

Conclusions

• In analytical processes, managing missing data is essential to preserving data integrity.
• We investigated a number of approaches to deal with missing data, such as imputation and deletion algorithms.
• Imputation techniques included mean, median, mode, random forest, MICE
• Deletion methods included listwise (row deletion) and feature selection (column deletion)
• It is necessary to comprehend the missing data mechanism (MCAR, MAR, MNAR) in order to choose the best imputation techniques.

Conclusions Continued

• The efficacy of every imputation technique was evaluated using evaluation measures such as RMSE and R-Squared on the Ozone dataset.
• The features of the dataset and the goals of the analysis should guide the choice of imputation technique.
• When deletion procedures are used and the missing data are not entirely random, skewed conclusions may arise.
• While single imputation techniques are straightforward, they may understate data variability.
• Subsequent investigations may delve deeper into sophisticated machine learning methodologies to enhance the precision of missing data imputation.